{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "vscode": {
     "languageId": "markdown"
    }
   },
   "source": [
    "# Hypothetical Document Embedding (HyDE) for RAG\n",
    "\n",
    "In this notebook, I implement HyDE (Hypothetical Document Embedding) - an innovative retrieval technique that transforms user queries into hypothetical answer documents before performing retrieval. This approach bridges the semantic gap between short queries and lengthy documents.\n",
    "\n",
    "Traditional RAG systems embed the user's short query directly, but this often fails to capture the semantic richness needed for optimal retrieval. HyDE solves this by:\n",
    "\n",
    "- Generating a hypothetical document that answers the query\n",
    "- Embedding this expanded document instead of the original query\n",
    "- Retrieving documents similar to this hypothetical document\n",
    "- Creating more contextually relevant answers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting Up the Environment\n",
    "We begin by importing necessary libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import json\n",
    "import fitz\n",
    "from openai import OpenAI\n",
    "import re\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting Up the OpenAI API Client\n",
    "We initialize the OpenAI client to generate embeddings and responses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the OpenAI client with the base URL and API key\n",
    "client = OpenAI(\n",
    "    base_url=\"https://api.studio.nebius.com/v1/\",\n",
    "    api_key=os.getenv(\"OPENAI_API_KEY\")  # Retrieve the API key from environment variables\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Document Processing Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_text_from_pdf(pdf_path):\n",
    "    \"\"\"\n",
    "    Extract text content from a PDF file with page separation.\n",
    "    \n",
    "    Args:\n",
    "        pdf_path (str): Path to the PDF file\n",
    "        \n",
    "    Returns:\n",
    "        List[Dict]: List of pages with text content and metadata\n",
    "    \"\"\"\n",
    "    print(f\"Extracting text from {pdf_path}...\")  # Print the path of the PDF being processed\n",
    "    pdf = fitz.open(pdf_path)  # Open the PDF file using PyMuPDF\n",
    "    pages = []  # Initialize an empty list to store the pages with text content\n",
    "    \n",
    "    # Iterate over each page in the PDF\n",
    "    for page_num in range(len(pdf)):\n",
    "        page = pdf[page_num]  # Get the current page\n",
    "        text = page.get_text()  # Extract text from the current page\n",
    "        \n",
    "        # Skip pages with very little text (less than 50 characters)\n",
    "        if len(text.strip()) > 50:\n",
    "            # Append the page text and metadata to the list\n",
    "            pages.append({\n",
    "                \"text\": text,\n",
    "                \"metadata\": {\n",
    "                    \"source\": pdf_path,  # Source file path\n",
    "                    \"page\": page_num + 1  # Page number (1-based index)\n",
    "                }\n",
    "            })\n",
    "    \n",
    "    print(f\"Extracted {len(pages)} pages with content\")  # Print the number of pages extracted\n",
    "    return pages  # Return the list of pages with text content and metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def chunk_text(text, chunk_size=1000, overlap=200):\n",
    "    \"\"\"\n",
    "    Split text into overlapping chunks.\n",
    "    \n",
    "    Args:\n",
    "        text (str): Input text to chunk\n",
    "        chunk_size (int): Size of each chunk in characters\n",
    "        overlap (int): Overlap between chunks in characters\n",
    "        \n",
    "    Returns:\n",
    "        List[Dict]: List of chunks with metadata\n",
    "    \"\"\"\n",
    "    chunks = []  # Initialize an empty list to store the chunks\n",
    "    \n",
    "    # Iterate over the text in steps of (chunk_size - overlap)\n",
    "    for i in range(0, len(text), chunk_size - overlap):\n",
    "        chunk_text = text[i:i + chunk_size]  # Extract the chunk of text\n",
    "        if chunk_text:  # Ensure we don't add empty chunks\n",
    "            chunks.append({\n",
    "                \"text\": chunk_text,  # Add the chunk text\n",
    "                \"metadata\": {\n",
    "                    \"start_pos\": i,  # Start position of the chunk in the original text\n",
    "                    \"end_pos\": i + len(chunk_text)  # End position of the chunk in the original text\n",
    "                }\n",
    "            })\n",
    "    \n",
    "    print(f\"Created {len(chunks)} text chunks\")  # Print the number of chunks created\n",
    "    return chunks  # Return the list of chunks with metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple Vector Store Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SimpleVectorStore:\n",
    "    \"\"\"\n",
    "    A simple vector store implementation using NumPy.\n",
    "    \"\"\"\n",
    "    def __init__(self):\n",
    "        self.vectors = []  # List to store vector embeddings\n",
    "        self.texts = []  # List to store text content\n",
    "        self.metadata = []  # List to store metadata\n",
    "    \n",
    "    def add_item(self, text, embedding, metadata=None):\n",
    "        \"\"\"\n",
    "        Add an item to the vector store.\n",
    "        \n",
    "        Args:\n",
    "            text (str): Text content\n",
    "            embedding (List[float]): Vector embedding\n",
    "            metadata (Dict, optional): Additional metadata\n",
    "        \"\"\"\n",
    "        self.vectors.append(np.array(embedding))  # Append the embedding as a numpy array\n",
    "        self.texts.append(text)  # Append the text content\n",
    "        self.metadata.append(metadata or {})  # Append the metadata or an empty dict if None\n",
    "    \n",
    "    def similarity_search(self, query_embedding, k=5, filter_func=None):\n",
    "        \"\"\"\n",
    "        Find the most similar items to a query embedding.\n",
    "        \n",
    "        Args:\n",
    "            query_embedding (List[float]): Query embedding vector\n",
    "            k (int): Number of results to return\n",
    "            filter_func (callable, optional): Function to filter results\n",
    "            \n",
    "        Returns:\n",
    "            List[Dict]: Top k most similar items\n",
    "        \"\"\"\n",
    "        if not self.vectors:\n",
    "            return []  # Return an empty list if there are no vectors\n",
    "        \n",
    "        # Convert query embedding to numpy array\n",
    "        query_vector = np.array(query_embedding)\n",
    "        \n",
    "        # Calculate similarities using cosine similarity\n",
    "        similarities = []\n",
    "        for i, vector in enumerate(self.vectors):\n",
    "            # Skip if doesn't pass the filter\n",
    "            if filter_func and not filter_func(self.metadata[i]):\n",
    "                continue\n",
    "                \n",
    "            # Calculate cosine similarity\n",
    "            similarity = np.dot(query_vector, vector) / (np.linalg.norm(query_vector) * np.linalg.norm(vector))\n",
    "            similarities.append((i, similarity))  # Append index and similarity score\n",
    "        \n",
    "        # Sort by similarity (descending)\n",
    "        similarities.sort(key=lambda x: x[1], reverse=True)\n",
    "        \n",
    "        # Return top k results\n",
    "        results = []\n",
    "        for i in range(min(k, len(similarities))):\n",
    "            idx, score = similarities[i]\n",
    "            results.append({\n",
    "                \"text\": self.texts[idx],  # Add the text content\n",
    "                \"metadata\": self.metadata[idx],  # Add the metadata\n",
    "                \"similarity\": float(score)  # Add the similarity score\n",
    "            })\n",
    "        \n",
    "        return results  # Return the list of top k results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_embeddings(texts, model=\"BAAI/bge-en-icl\"):\n",
    "    \"\"\"\n",
    "    Create embeddings for the given texts.\n",
    "    \n",
    "    Args:\n",
    "        texts (List[str]): Input texts\n",
    "        model (str): Embedding model name\n",
    "        \n",
    "    Returns:\n",
    "        List[List[float]]: Embedding vectors\n",
    "    \"\"\"\n",
    "    # Handle empty input\n",
    "    if not texts:\n",
    "        return []\n",
    "        \n",
    "    # Process in batches if needed (OpenAI API limits)\n",
    "    batch_size = 100\n",
    "    all_embeddings = []\n",
    "    \n",
    "    # Iterate over the input texts in batches\n",
    "    for i in range(0, len(texts), batch_size):\n",
    "        batch = texts[i:i + batch_size]  # Get the current batch of texts\n",
    "        \n",
    "        # Create embeddings for the current batch\n",
    "        response = client.embeddings.create(\n",
    "            model=model,\n",
    "            input=batch\n",
    "        )\n",
    "        \n",
    "        # Extract embeddings from the response\n",
    "        batch_embeddings = [item.embedding for item in response.data]\n",
    "        all_embeddings.extend(batch_embeddings)  # Add the batch embeddings to the list\n",
    "    \n",
    "    return all_embeddings  # Return all embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Document Processing Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_document(pdf_path, chunk_size=1000, chunk_overlap=200):\n",
    "    \"\"\"\n",
    "    Process a document for RAG.\n",
    "    \n",
    "    Args:\n",
    "        pdf_path (str): Path to the PDF file\n",
    "        chunk_size (int): Size of each chunk in characters\n",
    "        chunk_overlap (int): Overlap between chunks in characters\n",
    "        \n",
    "    Returns:\n",
    "        SimpleVectorStore: Vector store containing document chunks\n",
    "    \"\"\"\n",
    "    # Extract text from the PDF file\n",
    "    pages = extract_text_from_pdf(pdf_path)\n",
    "    \n",
    "    # Process each page and create chunks\n",
    "    all_chunks = []\n",
    "    for page in pages:\n",
    "        # Pass the text content (string) to chunk_text, not the dictionary\n",
    "        page_chunks = chunk_text(page[\"text\"], chunk_size, chunk_overlap)\n",
    "        \n",
    "        # Update metadata for each chunk with the page's metadata\n",
    "        for chunk in page_chunks:\n",
    "            chunk[\"metadata\"].update(page[\"metadata\"])\n",
    "        \n",
    "        all_chunks.extend(page_chunks)\n",
    "    \n",
    "    # Create embeddings for the text chunks\n",
    "    print(\"Creating embeddings for chunks...\")\n",
    "    chunk_texts = [chunk[\"text\"] for chunk in all_chunks]\n",
    "    chunk_embeddings = create_embeddings(chunk_texts)\n",
    "    \n",
    "    # Create a vector store to hold the chunks and their embeddings\n",
    "    vector_store = SimpleVectorStore()\n",
    "    for i, chunk in enumerate(all_chunks):\n",
    "        vector_store.add_item(\n",
    "            text=chunk[\"text\"],\n",
    "            embedding=chunk_embeddings[i],\n",
    "            metadata=chunk[\"metadata\"]\n",
    "        )\n",
    "    \n",
    "    print(f\"Vector store created with {len(all_chunks)} chunks\")\n",
    "    return vector_store"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hypothetical Document Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_hypothetical_document(query, desired_length=1000):\n",
    "    \"\"\"\n",
    "    Generate a hypothetical document that answers the query.\n",
    "    \n",
    "    Args:\n",
    "        query (str): User query\n",
    "        desired_length (int): Target length of the hypothetical document\n",
    "        \n",
    "    Returns:\n",
    "        str: Generated hypothetical document\n",
    "    \"\"\"\n",
    "    # Define the system prompt to instruct the model on how to generate the document\n",
    "    system_prompt = f\"\"\"You are an expert document creator. \n",
    "    Given a question, generate a detailed document that would directly answer this question.\n",
    "    The document should be approximately {desired_length} characters long and provide an in-depth, \n",
    "    informative answer to the question. Write as if this document is from an authoritative source\n",
    "    on the subject. Include specific details, facts, and explanations.\n",
    "    Do not mention that this is a hypothetical document - just write the content directly.\"\"\"\n",
    "\n",
    "    # Define the user prompt with the query\n",
    "    user_prompt = f\"Question: {query}\\n\\nGenerate a document that fully answers this question:\"\n",
    "    \n",
    "    # Make a request to the OpenAI API to generate the hypothetical document\n",
    "    response = client.chat.completions.create(\n",
    "        model=\"meta-llama/Llama-3.2-3B-Instruct\",  # Specify the model to use\n",
    "        messages=[\n",
    "            {\"role\": \"system\", \"content\": system_prompt},  # System message to guide the assistant\n",
    "            {\"role\": \"user\", \"content\": user_prompt}  # User message with the query\n",
    "        ],\n",
    "        temperature=0.1  # Set the temperature for response generation\n",
    "    )\n",
    "    \n",
    "    # Return the generated document content\n",
    "    return response.choices[0].message.content"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Complete HyDE RAG Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hyde_rag(query, vector_store, k=5, should_generate_response=True):\n",
    "    \"\"\"\n",
    "    Perform RAG using Hypothetical Document Embedding.\n",
    "    \n",
    "    Args:\n",
    "        query (str): User query\n",
    "        vector_store (SimpleVectorStore): Vector store with document chunks\n",
    "        k (int): Number of chunks to retrieve\n",
    "        generate_response (bool): Whether to generate a final response\n",
    "        \n",
    "    Returns:\n",
    "        Dict: Results including hypothetical document and retrieved chunks\n",
    "    \"\"\"\n",
    "    print(f\"\\n=== Processing query with HyDE: {query} ===\\n\")\n",
    "    \n",
    "    # Step 1: Generate a hypothetical document that answers the query\n",
    "    print(\"Generating hypothetical document...\")\n",
    "    hypothetical_doc = generate_hypothetical_document(query)\n",
    "    print(f\"Generated hypothetical document of {len(hypothetical_doc)} characters\")\n",
    "    \n",
    "    # Step 2: Create embedding for the hypothetical document\n",
    "    print(\"Creating embedding for hypothetical document...\")\n",
    "    hypothetical_embedding = create_embeddings([hypothetical_doc])[0]\n",
    "    \n",
    "    # Step 3: Retrieve similar chunks based on the hypothetical document\n",
    "    print(f\"Retrieving {k} most similar chunks...\")\n",
    "    retrieved_chunks = vector_store.similarity_search(hypothetical_embedding, k=k)\n",
    "    \n",
    "    # Prepare the results dictionary\n",
    "    results = {\n",
    "        \"query\": query,\n",
    "        \"hypothetical_document\": hypothetical_doc,\n",
    "        \"retrieved_chunks\": retrieved_chunks\n",
    "    }\n",
    "    \n",
    "    # Step 4: Generate a response if requested\n",
    "    if should_generate_response:\n",
    "        print(\"Generating final response...\")\n",
    "        response = generate_response(query, retrieved_chunks)\n",
    "        results[\"response\"] = response\n",
    "    \n",
    "    return results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Standard (Direct) RAG Implementation for Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def standard_rag(query, vector_store, k=5, should_generate_response=True):\n",
    "    \"\"\"\n",
    "    Perform standard RAG using direct query embedding.\n",
    "    \n",
    "    Args:\n",
    "        query (str): User query\n",
    "        vector_store (SimpleVectorStore): Vector store with document chunks\n",
    "        k (int): Number of chunks to retrieve\n",
    "        generate_response (bool): Whether to generate a final response\n",
    "        \n",
    "    Returns:\n",
    "        Dict: Results including retrieved chunks\n",
    "    \"\"\"\n",
    "    print(f\"\\n=== Processing query with Standard RAG: {query} ===\\n\")\n",
    "    \n",
    "    # Step 1: Create embedding for the query\n",
    "    print(\"Creating embedding for query...\")\n",
    "    query_embedding = create_embeddings([query])[0]\n",
    "    \n",
    "    # Step 2: Retrieve similar chunks based on the query embedding\n",
    "    print(f\"Retrieving {k} most similar chunks...\")\n",
    "    retrieved_chunks = vector_store.similarity_search(query_embedding, k=k)\n",
    "    \n",
    "    # Prepare the results dictionary\n",
    "    results = {\n",
    "        \"query\": query,\n",
    "        \"retrieved_chunks\": retrieved_chunks\n",
    "    }\n",
    "    \n",
    "    # Step 3: Generate a response if requested\n",
    "    if should_generate_response:\n",
    "        print(\"Generating final response...\")\n",
    "        response = generate_response(query, retrieved_chunks)\n",
    "        results[\"response\"] = response\n",
    "        \n",
    "    return results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Response Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_response(query, relevant_chunks):\n",
    "    \"\"\"\n",
    "    Generate a final response based on the query and relevant chunks.\n",
    "    \n",
    "    Args:\n",
    "        query (str): User query\n",
    "        relevant_chunks (List[Dict]): Retrieved relevant chunks\n",
    "        \n",
    "    Returns:\n",
    "        str: Generated response\n",
    "    \"\"\"\n",
    "    # Concatenate the text from the chunks to create context\n",
    "    context = \"\\n\\n\".join([chunk[\"text\"] for chunk in relevant_chunks])\n",
    "    \n",
    "    # Generate response using OpenAI API\n",
    "    response = client.chat.completions.create(\n",
    "        model=\"meta-llama/Llama-3.2-3B-Instruct\",\n",
    "        messages=[\n",
    "            {\"role\": \"system\", \"content\": \"You are a helpful assistant. Answer the question based on the provided context.\"},\n",
    "            {\"role\": \"user\", \"content\": f\"Context:\\n{context}\\n\\nQuestion: {query}\"}\n",
    "        ],\n",
    "        temperature=0.5,\n",
    "        max_tokens=500\n",
    "    )\n",
    "    \n",
    "    return response.choices[0].message.content"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compare_approaches(query, vector_store, reference_answer=None):\n",
    "    \"\"\"\n",
    "    Compare HyDE and standard RAG approaches for a query.\n",
    "    \n",
    "    Args:\n",
    "        query (str): User query\n",
    "        vector_store (SimpleVectorStore): Vector store with document chunks\n",
    "        reference_answer (str, optional): Reference answer for evaluation\n",
    "        \n",
    "    Returns:\n",
    "        Dict: Comparison results\n",
    "    \"\"\"\n",
    "    # Run HyDE RAG\n",
    "    hyde_result = hyde_rag(query, vector_store)\n",
    "    hyde_response = hyde_result[\"response\"]\n",
    "    \n",
    "    # Run standard RAG\n",
    "    standard_result = standard_rag(query, vector_store)\n",
    "    standard_response = standard_result[\"response\"]\n",
    "    \n",
    "    # Compare results\n",
    "    comparison = compare_responses(query, hyde_response, standard_response, reference_answer)\n",
    "    \n",
    "    return {\n",
    "        \"query\": query,\n",
    "        \"hyde_response\": hyde_response,\n",
    "        \"hyde_hypothetical_doc\": hyde_result[\"hypothetical_document\"],\n",
    "        \"standard_response\": standard_response,\n",
    "        \"reference_answer\": reference_answer,\n",
    "        \"comparison\": comparison\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def compare_responses(query, hyde_response, standard_response, reference=None):\n",
    "    \"\"\"\n",
    "    Compare responses from HyDE and standard RAG.\n",
    "    \n",
    "    Args:\n",
    "        query (str): User query\n",
    "        hyde_response (str): Response from HyDE RAG\n",
    "        standard_response (str): Response from standard RAG\n",
    "        reference (str, optional): Reference answer\n",
    "        \n",
    "    Returns:\n",
    "        str: Comparison analysis\n",
    "    \"\"\"\n",
    "    system_prompt = \"\"\"You are an expert evaluator of information retrieval systems.\n",
    "Compare the two responses to the same query, one generated using HyDE (Hypothetical Document Embedding) \n",
    "and the other using standard RAG with direct query embedding.\n",
    "\n",
    "Evaluate them based on:\n",
    "1. Accuracy: Which response provides more factually correct information?\n",
    "2. Relevance: Which response better addresses the query?\n",
    "3. Completeness: Which response provides more thorough coverage of the topic?\n",
    "4. Clarity: Which response is better organized and easier to understand?\n",
    "\n",
    "Be specific about the strengths and weaknesses of each approach.\"\"\"\n",
    "\n",
    "    user_prompt = f\"\"\"Query: {query}\n",
    "\n",
    "Response from HyDE RAG:\n",
    "{hyde_response}\n",
    "\n",
    "Response from Standard RAG:\n",
    "{standard_response}\"\"\"\n",
    "\n",
    "    if reference:\n",
    "        user_prompt += f\"\"\"\n",
    "\n",
    "Reference Answer:\n",
    "{reference}\"\"\"\n",
    "\n",
    "    user_prompt += \"\"\"\n",
    "\n",
    "Please provide a detailed comparison of these two responses, highlighting which approach performed better and why.\"\"\"\n",
    "\n",
    "    response = client.chat.completions.create(\n",
    "        model=\"meta-llama/Llama-3.2-3B-Instruct\",\n",
    "        messages=[\n",
    "            {\"role\": \"system\", \"content\": system_prompt},\n",
    "            {\"role\": \"user\", \"content\": user_prompt}\n",
    "        ],\n",
    "        temperature=0\n",
    "    )\n",
    "    \n",
    "    return response.choices[0].message.content\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_evaluation(pdf_path, test_queries, reference_answers=None, chunk_size=1000, chunk_overlap=200):\n",
    "    \"\"\"\n",
    "    Run a complete evaluation with multiple test queries.\n",
    "    \n",
    "    Args:\n",
    "        pdf_path (str): Path to the PDF document\n",
    "        test_queries (List[str]): List of test queries\n",
    "        reference_answers (List[str], optional): Reference answers for queries\n",
    "        chunk_size (int): Size of each chunk in characters\n",
    "        chunk_overlap (int): Overlap between chunks in characters\n",
    "        \n",
    "    Returns:\n",
    "        Dict: Evaluation results\n",
    "    \"\"\"\n",
    "    # Process document and create vector store\n",
    "    vector_store = process_document(pdf_path, chunk_size, chunk_overlap)\n",
    "    \n",
    "    results = []\n",
    "    \n",
    "    for i, query in enumerate(test_queries):\n",
    "        print(f\"\\n\\n===== Evaluating Query {i+1}/{len(test_queries)} =====\")\n",
    "        print(f\"Query: {query}\")\n",
    "        \n",
    "        # Get reference answer if available\n",
    "        reference = None\n",
    "        if reference_answers and i < len(reference_answers):\n",
    "            reference = reference_answers[i]\n",
    "        \n",
    "        # Compare approaches\n",
    "        result = compare_approaches(query, vector_store, reference)\n",
    "        results.append(result)\n",
    "    \n",
    "    # Generate overall analysis\n",
    "    overall_analysis = generate_overall_analysis(results)\n",
    "    \n",
    "    return {\n",
    "        \"results\": results,\n",
    "        \"overall_analysis\": overall_analysis\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_overall_analysis(results):\n",
    "    \"\"\"\n",
    "    Generate an overall analysis of the evaluation results.\n",
    "    \n",
    "    Args:\n",
    "        results (List[Dict]): Results from individual query evaluations\n",
    "        \n",
    "    Returns:\n",
    "        str: Overall analysis\n",
    "    \"\"\"\n",
    "    system_prompt = \"\"\"You are an expert at evaluating information retrieval systems.\n",
    "Based on multiple test queries, provide an overall analysis comparing HyDE RAG (using hypothetical document embedding)\n",
    "with standard RAG (using direct query embedding).\n",
    "\n",
    "Focus on:\n",
    "1. When HyDE performs better and why\n",
    "2. When standard RAG performs better and why\n",
    "3. The types of queries that benefit most from HyDE\n",
    "4. The overall strengths and weaknesses of each approach\n",
    "5. Recommendations for when to use each approach\"\"\"\n",
    "\n",
    "    # Create summary of evaluations\n",
    "    evaluations_summary = \"\"\n",
    "    for i, result in enumerate(results):\n",
    "        evaluations_summary += f\"Query {i+1}: {result['query']}\\n\"\n",
    "        evaluations_summary += f\"Comparison summary: {result['comparison'][:200]}...\\n\\n\"\n",
    "\n",
    "    user_prompt = f\"\"\"Based on the following evaluations comparing HyDE vs standard RAG across {len(results)} queries, \n",
    "provide an overall analysis of these two approaches:\n",
    "\n",
    "{evaluations_summary}\n",
    "\n",
    "Please provide a comprehensive analysis of the relative strengths and weaknesses of HyDE compared to standard RAG,\n",
    "focusing on when and why one approach outperforms the other.\"\"\"\n",
    "\n",
    "    response = client.chat.completions.create(\n",
    "        model=\"meta-llama/Llama-3.2-3B-Instruct\",\n",
    "        messages=[\n",
    "            {\"role\": \"system\", \"content\": system_prompt},\n",
    "            {\"role\": \"user\", \"content\": user_prompt}\n",
    "        ],\n",
    "        temperature=0\n",
    "    )\n",
    "    \n",
    "    return response.choices[0].message.content"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_results(query, hyde_result, standard_result):\n",
    "    \"\"\"\n",
    "    Visualize the results of HyDE and standard RAG approaches.\n",
    "    \n",
    "    Args:\n",
    "        query (str): User query\n",
    "        hyde_result (Dict): Results from HyDE RAG\n",
    "        standard_result (Dict): Results from standard RAG\n",
    "    \"\"\"\n",
    "    # Create a figure with 3 subplots\n",
    "    fig, axs = plt.subplots(1, 3, figsize=(20, 6))\n",
    "    \n",
    "    # Plot the query in the first subplot\n",
    "    axs[0].text(0.5, 0.5, f\"Query:\\n\\n{query}\", \n",
    "                horizontalalignment='center', verticalalignment='center',\n",
    "                fontsize=12, wrap=True)\n",
    "    axs[0].axis('off')  # Hide the axis for the query plot\n",
    "    \n",
    "    # Plot the hypothetical document in the second subplot\n",
    "    hypothetical_doc = hyde_result[\"hypothetical_document\"]\n",
    "    # Shorten the hypothetical document if it's too long\n",
    "    shortened_doc = hypothetical_doc[:500] + \"...\" if len(hypothetical_doc) > 500 else hypothetical_doc\n",
    "    axs[1].text(0.5, 0.5, f\"Hypothetical Document:\\n\\n{shortened_doc}\", \n",
    "                horizontalalignment='center', verticalalignment='center',\n",
    "                fontsize=10, wrap=True)\n",
    "    axs[1].axis('off')  # Hide the axis for the hypothetical document plot\n",
    "    \n",
    "    # Plot comparison of retrieved chunks in the third subplot\n",
    "    # Shorten each chunk text for better visualization\n",
    "    hyde_chunks = [chunk[\"text\"][:100] + \"...\" for chunk in hyde_result[\"retrieved_chunks\"]]\n",
    "    std_chunks = [chunk[\"text\"][:100] + \"...\" for chunk in standard_result[\"retrieved_chunks\"]]\n",
    "    \n",
    "    # Prepare the comparison text\n",
    "    comparison_text = \"Retrieved by HyDE:\\n\\n\"\n",
    "    for i, chunk in enumerate(hyde_chunks):\n",
    "        comparison_text += f\"{i+1}. {chunk}\\n\\n\"\n",
    "    \n",
    "    comparison_text += \"\\nRetrieved by Standard RAG:\\n\\n\"\n",
    "    for i, chunk in enumerate(std_chunks):\n",
    "        comparison_text += f\"{i+1}. {chunk}\\n\\n\"\n",
    "    \n",
    "    # Plot the comparison text in the third subplot\n",
    "    axs[2].text(0.5, 0.5, comparison_text, \n",
    "                horizontalalignment='center', verticalalignment='center',\n",
    "                fontsize=8, wrap=True)\n",
    "    axs[2].axis('off')  # Hide the axis for the comparison plot\n",
    "    \n",
    "    # Adjust layout to prevent overlap\n",
    "    plt.tight_layout()\n",
    "    # Display the plot\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation of Hypothetical Document Embedding (HyDE) vs. Standard RAG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting text from data/AI_Information.pdf...\n",
      "Extracted 15 pages with content\n",
      "Created 4 text chunks\n",
      "Created 4 text chunks\n",
      "Created 3 text chunks\n",
      "Created 3 text chunks\n",
      "Created 3 text chunks\n",
      "Created 3 text chunks\n",
      "Created 3 text chunks\n",
      "Created 3 text chunks\n",
      "Created 3 text chunks\n",
      "Created 3 text chunks\n",
      "Created 3 text chunks\n",
      "Created 3 text chunks\n",
      "Created 3 text chunks\n",
      "Created 4 text chunks\n",
      "Created 3 text chunks\n",
      "Creating embeddings for chunks...\n",
      "Vector store created with 48 chunks\n",
      "\n",
      "=== Processing query with HyDE: What are the main ethical considerations in artificial intelligence development? ===\n",
      "\n",
      "Generating hypothetical document...\n",
      "Generated hypothetical document of 3364 characters\n",
      "Creating embedding for hypothetical document...\n",
      "Retrieving 5 most similar chunks...\n",
      "Generating final response...\n",
      "\n",
      "=== HyDE Response ===\n",
      "The main ethical considerations in artificial intelligence development include:\n",
      "\n",
      "1. Bias and Fairness: Ensuring that AI systems are fair, non-discriminatory, and do not perpetuate existing biases present in the data they are trained on.\n",
      "\n",
      "2. Transparency and Explainability: Making AI decisions more understandable and assessable to build trust and accountability.\n",
      "\n",
      "3. Privacy and Data Protection: Ensuring responsible data handling, implementing privacy-preserving techniques, and complying with data protection regulations.\n",
      "\n",
      "4. Accountability and Responsibility: Establishing clear guidelines and frameworks for AI development and deployment to address potential harms and ensure ethical behavior.\n",
      "\n",
      "5. Job Displacement: Addressing the potential economic and social impacts of AI-driven automation.\n",
      "\n",
      "6. Autonomy and Control: Establishing guidelines and frameworks to ensure that AI systems are developed and deployed in a way that prioritizes human well-being and safety.\n",
      "\n",
      "7. Weaponization of AI: Addressing the risks associated with the potential use of AI in autonomous weapons systems.\n",
      "\n",
      "8. Respect for Human Rights: Prioritizing the respect and protection of human rights, particularly in the development and deployment of AI systems.\n",
      "\n",
      "These considerations are guided by principles of Ethical AI, which include respect for human rights, privacy, non-discrimination, and beneficence.\n",
      "\n",
      "=== Processing query with Standard RAG: What are the main ethical considerations in artificial intelligence development? ===\n",
      "\n",
      "Creating embedding for query...\n",
      "Retrieving 5 most similar chunks...\n",
      "Generating final response...\n",
      "\n",
      "=== Standard RAG Response ===\n",
      "The main ethical considerations in artificial intelligence development include:\n",
      "\n",
      "1. Bias and Fairness: Ensuring that AI systems do not inherit and amplify biases present in the data they are trained on, leading to unfair or discriminatory outcomes.\n",
      "2. Transparency and Explainability: Making it possible to understand how AI systems arrive at their decisions and ensuring that these decisions are transparent and accountable.\n",
      "3. Respect for Human Rights, Privacy, Non-Discrimination, and Beneficence: Guiding the development and deployment of AI systems to ensure they are fair, transparent, accountable, and beneficial to society.\n",
      "4. Job Displacement: Addressing the potential economic and social impacts of AI-driven automation, particularly in industries with repetitive or routine tasks.\n",
      "5. Autonomy and Control: Establishing clear guidelines and ethical frameworks for AI development and deployment, particularly as AI systems become more autonomous.\n",
      "6. Weaponization of AI: Addressing the risks associated with AI-powered autonomous weapons and establishing international discussions and regulations.\n",
      "7. Cybersecurity: Protecting sensitive information and ensuring responsible data handling, as well as using AI to detect and respond to threats, analyze network traffic, and identify vulnerabilities.\n",
      "\n",
      "These considerations are essential to ensure that AI development aligns with societal values and promotes the well-being of individuals and society as a whole.\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 2000x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting text from data/AI_Information.pdf...\n",
      "Extracted 15 pages with content\n",
      "Created 4 text chunks\n",
      "Created 4 text chunks\n",
      "Created 3 text chunks\n",
      "Created 3 text chunks\n",
      "Created 3 text chunks\n",
      "Created 3 text chunks\n",
      "Created 3 text chunks\n",
      "Created 3 text chunks\n",
      "Created 3 text chunks\n",
      "Created 3 text chunks\n",
      "Created 3 text chunks\n",
      "Created 3 text chunks\n",
      "Created 3 text chunks\n",
      "Created 4 text chunks\n",
      "Created 3 text chunks\n",
      "Creating embeddings for chunks...\n",
      "Vector store created with 48 chunks\n",
      "\n",
      "\n",
      "===== Evaluating Query 1/1 =====\n",
      "Query: How does neural network architecture impact AI performance?\n",
      "\n",
      "=== Processing query with HyDE: How does neural network architecture impact AI performance? ===\n",
      "\n",
      "Generating hypothetical document...\n",
      "Generated hypothetical document of 3438 characters\n",
      "Creating embedding for hypothetical document...\n",
      "Retrieving 5 most similar chunks...\n",
      "Generating final response...\n",
      "\n",
      "=== Processing query with Standard RAG: How does neural network architecture impact AI performance? ===\n",
      "\n",
      "Creating embedding for query...\n",
      "Retrieving 5 most similar chunks...\n",
      "Generating final response...\n",
      "\n",
      "=== OVERALL ANALYSIS ===\n",
      "**Overall Analysis: HyDE RAG vs Standard RAG**\n",
      "\n",
      "Based on the evaluation of Query 1, we can observe that both HyDE RAG and standard RAG provide accurate information about the impact of neural network architecture on AI performance. However, a more in-depth analysis of the strengths and weaknesses of each approach reveals the following:\n",
      "\n",
      "**Strengths and Weaknesses of HyDE RAG:**\n",
      "\n",
      "Strengths:\n",
      "\n",
      "1. **Improved contextual understanding**: HyDE RAG's use of document embedding allows it to capture the context and relationships between different concepts, leading to more accurate and informative responses.\n",
      "2. **Better handling of complex queries**: HyDE RAG's ability to represent documents as vectors enables it to capture the nuances of complex queries, such as the impact of neural network architecture on AI performance.\n",
      "\n",
      "Weaknesses:\n",
      "\n",
      "1. **Higher computational requirements**: HyDE RAG's use of document embedding requires more computational resources, which can lead to slower response times.\n",
      "2. **Overfitting to training data**: HyDE RAG's reliance on document embedding can lead to overfitting to the training data, which can result in poor performance on unseen queries.\n",
      "\n",
      "**Strengths and Weaknesses of Standard RAG:**\n",
      "\n",
      "Strengths:\n",
      "\n",
      "1. **Faster response times**: Standard RAG's use of direct query embedding allows it to respond quickly, even to complex queries.\n",
      "2. **Robustness to overfitting**: Standard RAG's direct query embedding approach is less prone to overfitting, as it does not rely on document representation.\n",
      "\n",
      "Weaknesses:\n",
      "\n",
      "1. **Limited contextual understanding**: Standard RAG's direct query embedding approach can lead to limited contextual understanding, resulting in less accurate and informative responses.\n",
      "2. **Difficulty handling complex queries**: Standard RAG's direct query embedding approach can struggle to capture the nuances of complex queries, such as the impact of neural network architecture on AI performance.\n",
      "\n",
      "**When HyDE RAG Outperforms Standard RAG:**\n",
      "\n",
      "HyDE RAG is likely to outperform Standard RAG in the following scenarios:\n",
      "\n",
      "1. **Complex queries**: HyDE RAG's ability to capture the nuances of complex queries, such as the impact of neural network architecture on AI performance, makes it a better choice for these types of queries.\n",
      "2. **Contextual understanding**: HyDE RAG's improved contextual understanding, enabled by document embedding, makes it a better choice when the query requires a deep understanding of the context and relationships between different concepts.\n",
      "\n",
      "**When Standard RAG Outperforms HyDE RAG:**\n",
      "\n",
      "Standard RAG is likely to outperform HyDE RAG in the following scenarios:\n",
      "\n",
      "1. **Simple queries**: Standard RAG's direct query embedding approach can respond quickly and accurately to simple queries, making it a better choice for these types of queries.\n",
      "2. **Real-time applications**: Standard RAG's faster response times make it a better choice for real-time applications, such as search engines or chatbots.\n",
      "\n",
      "**Recommendations:**\n",
      "\n",
      "1. **Use HyDE RAG for complex queries**: When dealing with complex queries that require a deep understanding of the context and relationships between different concepts, HyDE RAG is a better choice.\n",
      "2. **Use Standard RAG for simple queries**: When dealing with simple queries that require a quick and accurate response, Standard RAG is a better choice.\n",
      "3. **Use HyDE RAG for applications requiring contextual understanding**: When the application requires a deep understanding of the context and relationships between different concepts, HyDE RAG is a better choice.\n",
      "4. **Use Standard RAG for applications requiring real-time responses**: When the application requires a quick and accurate response, Standard RAG is a better choice.\n"
     ]
    }
   ],
   "source": [
    "# Path to the AI information document\n",
    "pdf_path = \"data/AI_Information.pdf\"\n",
    "\n",
    "# Process document and create vector store\n",
    "# This loads the document, extracts text, chunks it, and creates embeddings\n",
    "vector_store = process_document(pdf_path)\n",
    "\n",
    "# Example 1: Direct comparison for a single query related to AI\n",
    "query = \"What are the main ethical considerations in artificial intelligence development?\"\n",
    "\n",
    "# Run HyDE RAG approach\n",
    "# This generates a hypothetical document answering the query, embeds it, \n",
    "# and uses that embedding to retrieve relevant chunks\n",
    "hyde_result = hyde_rag(query, vector_store)\n",
    "print(\"\\n=== HyDE Response ===\")\n",
    "print(hyde_result[\"response\"])\n",
    "\n",
    "# Run standard RAG approach for comparison\n",
    "# This directly embeds the query and uses it to retrieve relevant chunks\n",
    "standard_result = standard_rag(query, vector_store)\n",
    "print(\"\\n=== Standard RAG Response ===\")\n",
    "print(standard_result[\"response\"])\n",
    "\n",
    "# Visualize the differences between HyDE and standard RAG approaches\n",
    "# Shows the query, hypothetical document, and retrieved chunks side by side\n",
    "visualize_results(query, hyde_result, standard_result)\n",
    "\n",
    "# Example 2: Run full evaluation with multiple AI-related queries\n",
    "test_queries = [\n",
    "    \"How does neural network architecture impact AI performance?\"\n",
    "]\n",
    "\n",
    "# Optional reference answers for better evaluation\n",
    "reference_answers = [\n",
    "    \"Neural network architecture significantly impacts AI performance through factors like depth (number of layers), width (neurons per layer), connectivity patterns, and activation functions. Different architectures like CNNs, RNNs, and Transformers are optimized for specific tasks such as image recognition, sequence processing, and natural language understanding respectively.\",\n",
    "]\n",
    "\n",
    "# Run comprehensive evaluation comparing HyDE and standard RAG approaches\n",
    "evaluation_results = run_evaluation(\n",
    "    pdf_path=pdf_path,\n",
    "    test_queries=test_queries,\n",
    "    reference_answers=reference_answers\n",
    ")\n",
    "\n",
    "# Print the overall analysis of which approach performs better across queries\n",
    "print(\"\\n=== OVERALL ANALYSIS ===\")\n",
    "print(evaluation_results[\"overall_analysis\"])"
   ]
  }
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